Xf_last_runtime3, Xf_last_runtime4, Xf_usermean))
#At this point we have: Xf, yf, tsafir
#___LEARNING___
print("encoding finished. predicting..")
if tool == "tsafrir":
#___TSAFRIR MEAN___
def bound_req(pred, req):
if pred < req:
return pred
else:
return req
bound_with_reqtime = np.vectorize(bound_req)
np_array_to_file(bound_with_reqtime(tsafir, data['time_req']),
arguments["<output_folder>"] + "/prediction_tsafrir")
elif tool in ["sgd", "passive-aggressive"]:
#___ONLINE LEARNING___
from simpy import Environment, simulate, Monitor
from swfpy import io
if arguments['--verbose'] == True:
import logging
from simpy.util import start_delayed
if arguments['--verbose'] == True:
global_logger = logging.getLogger('global')
hdlr = logging.FileHandler('predictor.log')
formatter = logging.Formatter('%(levelname)s %(message)s')
hdlr.setFormatter(formatter)
global_logger.addHandler(hdlr)
Xf_last_runtime4,
Xf_usermean))
#At this point we have: Xf, yf, tsafir
#___LEARNING___
print("encoding finished. predicting..")
if tool=="tsafrir":
#___TSAFRIR MEAN___
def bound_req(pred,req):
if pred<req:
return pred
else:
return req
bound_with_reqtime=np.vectorize(bound_req)
np_array_to_file(bound_with_reqtime(tsafir,data['time_req']),arguments["<output_folder>"]+"/prediction_tsafrir")
elif tool in ["sgd","passive-aggressive"]:
#___ONLINE LEARNING___
from simpy import Environment,simulate,Monitor
from swfpy import io
if arguments['--verbose']==True:
import logging
from simpy.util import start_delayed
if arguments['--verbose']==True:
global_logger = logging.getLogger('global')
hdlr = logging.FileHandler('predictor.log')
formatter = logging.Formatter('%(levelname)s %(message)s')
hdlr.setFormatter(formatter)
global_logger.addHandler(hdlr)
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
max_features='auto',
bootstrap=True,
oob_score=False,
n_jobs=3,
random_state=None,
verbose=0,
min_density=None,
compute_importances=None)
print("learning random forests")
forest.fit(Xlearn, ylearn)
print("prediction")
pred = forest.predict(Xtest)
np_array_to_file(pred, "prediction_rf")
elif tool == "tsafrir":
#___TSAFRIR MEAN___
np_array_to_file(tsafir, "prediction_tsafrir")
elif tool == "svr":
#___OFFLINE SVR___
print("creating SVR")
svr = SVR(kernel='linear',
degree=3,
gamma=0.0,
coef0=0.0,
tol=0.001,
C=1.0,
epsilon=0.1,
if tool=="random_forest":
#____OFFLINE RANDOM FORESTS____
start=int(len(Xf)*.7)
i=int(len(Xf)*.8)
Xlearn=Xf[start:i:1,:]
Xtest=Xf[i:len(Xf),:]
ylearn=yf[start:i:1]
ytest=yf[i:len(yf)]
tsafirtest=tsafir[i:len(yf)]
forest=RandomForestRegressor(n_estimators=40, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features='auto', bootstrap=True, oob_score=False, n_jobs=3, random_state=None, verbose=0, min_density=None, compute_importances=None)
print("learning random forests")
forest.fit(Xlearn,ylearn)
print("prediction")
pred=forest.predict(Xtest)
np_array_to_file(pred,"prediction_rf")
elif tool=="tsafrir":
#___TSAFRIR MEAN___
np_array_to_file(tsafir,"prediction_tsafrir")
elif tool=="svr":
#___OFFLINE SVR___
print("creating SVR")
svr=SVR(kernel='linear', degree=3, gamma=0.0, coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, probability=False, cache_size=200, verbose=False, max_iter=-1, random_state=None)
svr.fit(Xlearn,ylearn)
pred=svr.predict(Xtest)
np_array_to_file(pred,"prediction_rf")
elif tool in ["sgd","passive-aggressive"]:
#___ONLINE LEARNING___
